Kelp: A New Streaming Log Parser Algorithm (www.stonebucklabs.com)

A new algorithm called Kelp for parsing logs has been introduced, offering a significant advancement in log processing capabilities. Unlike existing methods that often rely on regex or batch processing, Kelp is designed as a streaming algorithm, enabling real-time log analysis. By effectively segregating log messages based on their token lengths and leveraging a hashmap to track word frequencies in consistent column positions, Kelp improves upon the accuracy and efficiency of log template generation. This allows it to handle the dynamic nature of logs more adeptly than previous algorithms. The significance of Kelp for the AI/ML community lies in its ability to simplify and expedite log parsing—a critical need for organizations generating vast amounts of log data. By facilitating a "run and forget" approach, Kelp eliminates the operational burden of periodic parsing, making it easier to adapt to new log events. This innovative streaming technique addresses common challenges such as the misclassification of log templates and the complexities of identifying static versus dynamic elements in log messages, ultimately enhancing the analytics capabilities of systems reliant on log data for insights and operations.